The rapid expansion of herbicide use in smallholder agriculture in Ethiopia: Patterns, drivers, and implications
Bibliographic record
Abstract
We use qualitative and quantitative information from a number of datasets to study the adoption patterns and labor productivity impacts of herbicide use in Ethiopia. We find a four-fold increase in the value of herbicides imported into Ethiopia over the last decade, primarily by the private-sector. Adoption of herbicides by smallholders has grown rapidly over this period, with the application of herbicides on cereals doubling to more than a quarter of the area under cereals between 2004 and 2014. Relying on unique data from a large-scale survey of producers of teff, the most widely grown cereal in Ethiopia, we find significant positive labor productivity effects of herbicide use of between 9 and 18 percent. We show that the adoption of herbicides is strongly related to proximity to urban centers, levels of local rural wages, and access to markets. All these factors have changed significantly over the last decade in Ethiopia, explaining the rapid take-off in herbicide adoption. The significant increase in herbicide use in Ethiopia has important implications for rural labor markets, potential environmental and health considerations, and capacity development for the design and effective implementation of regulatory policies on herbicides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".